Consumers want to find products and services, such as books, movies, restaurants, hotels, and online merchants, that are highly regarded and that fit their particular needs. A growing body of user-generated product reviews exists which include information useful to a consumer when making their own purchase decisions. However, the body of product reviews can be immense and difficult to navigate, making the identification of pertinent information difficult.
Many attempts have been undertaken to utilize user-generated reviews to provide recommendations to consumers. However, these attempts have been less than successful for a number of reasons. Product and service providers can influence the recommendations by providing positive reviews of their own, or incentivizing customers to do so. Product reviews often can suffer from selection bias induced by the fact that consumers are more likely to leave reviews when they have an extremely positive or extremely negative experience. In addition, it can be difficult to account for personal differences. One individual may attach varying levels of importance to different aspects of a product or service, which means what may appeal to one person may not appeal, or may be less important to another individual. The most common approach to personalized recommendations often is referred to as “collaborative filtering,” where reviews from people with similar expressed preferences are given more weight. However, it is difficult to provide good score-based feedback on products or services with a low number of product reviews.
Accordingly, a need in the art exists for systems and methods that can aggregate and present such product information to the consumer in an informative and readily accessible manner.